Spectral kernel machines with electrically tunable photodetectors

Spectral machine vision collects spectral and spatial information as dense 3D hypercubes and digitally processes them into scene recognition, which causes a data bottleneck, limiting power efficiency, frame rate, and spectral-spatial resolution. This work introduces a device architecture called spectral kernel machines (SKM) to overcome these bottlenecks. SKM directly compresses spectral analysis through the output photocurrent and learns from example objects to identify and classify new samples in a 'sniff-and-seek' mode. We experimentally demonstrated SKM with electrically tunable bipolar black phosphorous (bP)-MoS2 photodiodes in the near/mid-infrared band and silicon photoconductors in the visible band, performing versatile intelligent tasks from chemometrics to semiconductor metrology. This architecture consumes significantly lower power and is more than an order of magnitude faster than existing solutions for hyperspectral image analysis, defining an intelligent imaging and sensing paradigm with intriguing possibilities.